The QAM scheme is capable of transmissions loading two or more bits onto a given waveform symbol, whose waveform can be mathematically expressed in two real numbers and imaginary numbers that do not interfere with each other. That is, in the complex number imaginary number α+βi, a change of the value a does not affect the value β. Due to that reason, a quadrature signal component can correspond to α, and an in-phase signal component can correspond to β. Generally, the quadrature signal component is referred to as the Q-channel, and the in-phase component signal is referred to as the I-channel.
A constellation diagram of QAM plots the amplitudes of such two waves with respect to each other so as to make a-number of combinations. The positions of the combinations on a complex number plane should have an equal conditional probability. FIG. 2 is a diagram showing an example of such a constellation diagram, whose size is 16 combinations. Also, each of the points shown in FIG. 2 is referred to as a constellation point. Also, the binary number written under each constellation point represent the symbol assigned to that point, that is, a bundle of bits.
Generally, a QAM demodulator serves to convert signals incoming on an I channel and an Q channel, that is, a received signal given as α+βi, into the original bit bundle according to the constellation points mentioned above, that is, the constellation diagram. However, the received signals may not be positioned on places assigned previously, in most cases due to the effect of noise interference, and accordingly the demodulator has to restore the signals that have been converted due to noise. Since it is often desirable to guarantee the reliability of communication in that the demodulator takes charge of the role of noise cancellation, it is possible to embody a more effective and reliable communication system by rendering the role to the next step of a channel decoder. However, since there is an information loss in a bit quantization process performed by a binary bit detector as in a hard decision by converting a demodulation signal having a continuous value to corresponding discrete signals of 2 levels in order to perform such a process, a similarity measure with respect to the distance between a received signal and the constellation point is changed from a Hamming distance to a Euclidean distance without using the binary, bit detector, so that an additional gain can be obtained.
As shown in FIG. 1, in order to modulate and transmit a signal encoded by a channel encoder and demodulate the signal in a channel demodulator through a hard decision coding process, the demodulator has to have a scheme for generating the hard decision values corresponding to each of the output bits of a channel encoder from a receiving signal consisted of an in-phase signal component and a quadrature phase signal component. Such scheme generally includes two procedures, that is, a simple metric procedure proposed by Nokia company and a dual minimum metric procedure proposed by Motorola, both procedures calculating LLR (Log Likelihood Ratio) with respect to each of the output bits and using it as an input soft decision value of the channel demodulator.
The simple metric procedure is an event algorithm that transforms a complicated LLR calculation equation to a simple form of an approximation equation, which has a degradation of performance due to an LLR distortion caused by using the approximation equation even though it makes the LLR calculation simple. On the other hand, the dual minimum metric procedure is an event algorithm that uses the LLR calculated using a more precise approximation equation as an input of the channel demodulator, which has the merit of considerably improving the degradation of performance caused in the case of using the simple metric procedure, but it has an expected problem that more calculations are needed compared with the simple metric procedure and an its complication is considerably increased upon embodying hardware.